TL;DR
- Dun & Bradstreet is the first shortlist call when the difficult question is which legal entity belongs to which corporate parent?
- Crunchbase is strongest when a funding round, acquisition or other private-market development is the reason to research an account now.
- People Data Labs fits teams that need to match a known company to a documented record through an API and control exactly which fields enter the CRM.
- Coresignal is worth testing when a data team wants structured company records through an API or a larger dataset arrangement.
- Buy on the number of accepted account records, not the headline number of companies. None of these purchases automatically gives you a current, reachable decision-maker.
What each provider is actually for
The question is rarely “which database is biggest?” An account researcher needs to know whether a record represents the company a rep can sell to, whether its facts are recent enough for the intended decision, and whether the contract permits those facts to be used in the planned workflow. A provider can be excellent at one of those jobs and only adequate at another.
| Provider | Primary reason to shortlist | Record to inspect first | Commercial question to settle |
|---|---|---|---|
| Dun & Bradstreet | Legal-entity identity and corporate family | A known subsidiary and its ultimate parent | Which linkage and geography products are in the quote? |
| Crunchbase | Private-company context and funding events | A recently funded company and one similar unfunded account | Does the API or data licence permit your use and exports? |
| People Data Labs | Deterministic API matching and field-level mapping | A company with a rebrand or several domains | Which field bundles are included in the company plan? |
| Coresignal | Structured company records and larger data workflows | A company with several locations or inconsistent profiles | Which endpoint, record type and plan gates apply? |
These are company data options. If the job is to find and verify named buyers, include a separate contact source and its costs. Our B2B data enrichment tools comparison covers that adjacent purchase. Combining the two budgets into one “cost per lead” figure hides where a workflow is failing.
Define the account before buying data
Imagine that Meridian Holdings owns Meridian Payments in Britain and Meridian Commerce in Singapore. The sales team has a contract with one subsidiary, while finance reports revenue against the parent. A domain match to meridian.com could be technically correct and still put an opportunity on the wrong account. Before evaluating a vendor, decide whether your CRM account represents a legal entity, a brand, a regional operating company, or a selling unit. Then record the parent relationship separately.
For a useful test record, retain the original company name and domain, the vendor's persistent identifier, accepted website and alternate domains, legal/trading name, country, parent identifier, field-level source, observation date and match decision. “Employee count: 500” without the entity and date is not a reliable territory rule. It might describe a local subsidiary, a LinkedIn page or the whole group. Revenue can be reported, modelled or absent; those states should not collapse into one apparently precise number.
Keep events separate from attributes. A funding round needs its own event date, round type and source. A company-level “total funding” field is convenient for filtering but cannot answer whether a round occurred last week or three years ago. The same distinction applies to acquisitions, job openings and technology changes. A salesperson can then explain why now without presenting a stale event as a current buying signal.
1. Dun & Bradstreet: corporate identity and hierarchy
D&B is the specialist choice when account research has to reconcile companies across countries, systems and reporting levels. Its D-U-N-S identifier gives a stable key for a business record. More relevant to a sales team, its Corporate Linkage documentation describes an enhanced product that returns the ultimate parent and related entities in a family tree. That is a specific product capability, not a promise that every D&B subscription supplies every hierarchy field.
Where it earns its place. An enterprise seller may already know the brand but need to establish the contracting entity, headquarters, domestic parent and ultimate parent before assigning territory or pursuing a group agreement. A procurement or risk team may need the same identity outside the sales CRM. If one vendor's record is “Meridian” and another's is “Meridian UK Ltd”, an identifier and family relationship are more useful than another broad industry label.
Where it disappoints. A robust legal record does not prove that a newly launched division has a current budget, active project or reachable buyer. The firmographic and linkage coverage that matters will depend on the product, geography and segment bought. A hierarchy can also conflict with the organisation's selling policy: the parent may control procurement while each subsidiary owns a separate budget. D&B can describe the relationship; it cannot decide which level a rep should own.
What to ask for. Submit 20 known parent–subsidiary pairs, including regional entities and recent reorganisations. Request the precise API or file product, returned fields, update method, country coverage, matching process, permitted CRM use and quote for the volume you will actually run. Ask how conflicts are flagged, not merely whether a record can be found. D&B pricing is quote-led for this use case, so an unverified public “price per company” would be false precision.
Best fit: multinational account lists where duplicate entities and mistaken parent assignment cost more than enrichment itself. Poor fit: a small team whose primary need is to discover freshly funded startups and immediately contact founders.
2. Crunchbase: funding-led account research
Crunchbase is a different purchase. Its API offering makes company and private-market information available for data workflows. In an outbound motion, its useful edge is often the sequence of events: financing, investors, acquisitions and company development. Those facts can make a research queue timely. A funding announcement alone is not evidence that a particular software category has approved budget.
Where it earns its place. Suppose you sell finance operations software to Series B–D technology companies. You can start with a new round, confirm the company identity, then research headcount change, international expansion and existing systems. The funding event is a prompt to investigate, not a ready-made personalised email. Crunchbase is also helpful when a market-mapping team wants comparable private-company context rather than only a CRM field fill.
Where it disappoints. Venture-backed companies are more likely to leave rich funding trails than mature private manufacturers, regional subsidiaries or public agencies. A prospecting team selling primarily to those latter groups should not assume missing funding information means an account is unimportant. Likewise, predictive labels should be treated as a provider's model output, not a fact about buyer intent. Inspect the underlying dated event before using it in outreach.
What to ask for. Check API access, fields, export rights, refresh process and the distinction between an individual research subscription and a licensed data workflow. Price your real use case: internal CRM enrichment, analyst export and customer-facing redistribution may have different rights. Ask for a sample in your target segment with both funded and unfunded companies, and compare the event date and company identity against the source announcement.
Best fit: territory creation or account prioritisation where private-market events matter. Poor fit: legal-family mapping or universal firmographic coverage. If both jobs matter, use Crunchbase's event as one input and another source for the account hierarchy; do not let the newest company event overwrite the CRM's confirmed legal entity.
3. People Data Labs: API-first company matching
People Data Labs offers Company Enrichment for matching an input company to a structured record and Company Search for finding records by criteria. Its published company schema helps an operations team decide which fields are worth ingesting before it writes integration code. That transparency is valuable when the hard part is reliable mapping, not a sales-interface shortlist.
Where it earns its place. A revenue-operations team already has 20,000 accounts, partial domains and a rule that only approved fields may be changed. It can send a sample through enrichment, retain the returned identifier and compare names, domains, locations and parent evidence before accepting a match. A separate search query can construct new candidate lists. These endpoints solve different problems: enrichment starts with a known account; search can return several companies and needs deduplication.
Where it disappoints. A rich schema is not proof that every field is populated for your companies or included in your plan. Domain-only matching can confuse brands and subsidiaries. The engineering team still needs field ownership, exception queues and a refresh job. If sales users want a fully managed research interface rather than API integration, include implementation time in the comparison.
PDL's self-serve plan documentation currently lists Company Pro starting at $100 per month for 1,000 or more monthly company records. The company plan is separate from Person Pro. The documented rule is one credit per successful Company Enrichment match and one credit per profile returned in Company Search; unsuccessful enrichment requests are not billed under the stated rule. That does not mean every attempted request produces a usable account. A technically successful match can still be the wrong subsidiary and consume a credit.
What to ask for. Confirm the field bundle, monthly carryover or expiry, volume above the self-serve tier, rate limits, data rights and whether a bulk licence fits better. In a 300-account pilot, log the raw request and response beside the human verdict. If 270 requests return records but only 225 represent the correct selling entities, the useful number for account research is 225, not 270.
Best fit: a team with API capacity that wants controlled enrichment or search. Poor fit: a buyer expecting the plan to supply a finished contact list and seller workflow without data engineering.
4. Coresignal: structured records at API or dataset scale
Coresignal's pricing and product page distinguishes database APIs from larger datasets and lists company records as credit-consuming retrievals. That makes it relevant for an internal research layer or data product that needs records beyond a rep-by-rep lookup. Evaluate the actual company endpoint and record type, because a search result, a base record and a multi-source record are not interchangeable purchases.
Where it earns its place. A research team may want to maintain its own company table, attach source-specific fields, refresh regularly and join company, employment or job-posting observations. A structured API or dataset can feed that workflow. It is especially useful to test if several sources disagree about domain, industry or headcount, because the disagreement itself can be retained for review instead of being hidden by a single CRM overwrite.
Where it disappoints. A large record collection is only as valuable as the identity resolution and permitted use built around it. Employee-derived observations may help estimate size or change but should not be presented as audited legal-entity headcount. Data delivery, update cadence and redistribution rights may be different for API access and a dataset agreement. A company record is not a verified contact.
The published comparison table lists Mini at $49 per month with 2,500 credits, Starter at $199 with 12,000, and higher tiers. The same page describes a company record as 10–20 credits, depending on record type. At 10 credits each, 2,500 credits is a theoretical 250 base records; at 20, it is 125 multi-source records. That is capacity arithmetic, not a guarantee of matches, endpoint entitlement or usable records. Check the current plan comparison and checkout for the exact endpoint, search/preview charges, rollover and record definition. A dataset purchase is a separate commercial discussion.
What to ask for. Retrieve the same 50 companies through the exact endpoint you intend to deploy. Record credits consumed, identifier persistence, field values, source/observation dates and changed records after a refresh. Test company names with diacritics, holding companies and firms that operate several websites. Then compare API economics with a quote for the intended dataset volume.
Best fit: a data team comfortable operating a company table and monitoring quality. Poor fit: a buyer who wants a small, fully worked sales prospecting list with verified personal contact details.
A 300-account test that exposes bad matches
An attractive demo normally uses famous companies. That is not the data you will struggle to sell into. Build a blind sample of 300 real target accounts before requesting trials or quotes:
- 100 ordinary target accounts from the CRM, chosen across your actual countries, industries and employee bands.
- 100 edge cases: subsidiaries, franchises, holding companies, renamed businesses, local legal entities, shared domains and businesses with non-Latin or accented names.
- 100 recent or less visible accounts, including new companies and firms the team has not researched recently.
Freeze the input names, domains and known parent relationships. For each provider, submit the same inputs through the product and plan you might buy. Have a reviewer who did not see the provider's confidence score label each return correct entity, wrong entity, ambiguous or no match. Separate “record returned” from “record accepted.” Log the fields the team would actually use: website, location, segment, parent, funding event and observation date. Mark an unknown value as unknown; do not fill it from another provider and then credit the tested source.
Then run a small refresh: repeat 30 records after an agreed interval and check which changes are detectable. Ask whether the supplier gives deltas, a full replacement or on-demand queries. If an analyst must manually resolve every changed parent, include those minutes in operating cost. If a contract restricts storing or redistributing a field, that field is not useful for the restricted workflow even when the API returns it.
The winning provider is the one that yields the most decision-ready, permitted records for your segment at an acceptable cost. There need not be one winner across identity, events and contacts. A scoped D&B identity layer plus a Crunchbase event feed, for example, might outperform either alone if the sales motion needs both. That is an architecture choice to test, not an endorsement to buy two subscriptions by default.
Cost per accepted account
Use the same denominator for every quote:
Cost per accepted account = (data fee + API or export charges + integration labour + exception review) ÷ correctly matched, permitted and decision-useful accounts.
Suppose the 300-account test costs $150 in data and $300 in staff time. The API returns 270 records, but 45 refer to the wrong legal entity and 25 lack the field needed for your targeting rule. You have 200 accepted accounts, so the pilot costs $2.25 per accepted account, not $0.50 per attempted lookup or $0.56 per returned record. The exact cost of an annual enterprise licence needs the same calculation over an expected year of use, including seats, country packs, API volume, implementation and renewal terms.
Do not compare a $49 API entry plan directly with a quote for broad corporate linkage or data redistribution. They include different rights and solve different problems. PDL's successful-match billing and Coresignal's 10–20-credit company retrievals also use different units. For a fair budget, model the number of search results, enrichments, reruns and manual exceptions you expect. Add contact discovery and email verification only if the campaign actually requires them; those are separate stages. Our guide to B2B data APIs looks at that wider discovery-to-enrichment chain.
Which provider should you choose?
Choose D&B when parentage, legal identity and cross-system reconciliation are blocking account ownership. Choose Crunchbase when dated private-market events are central to territory selection or timely research. Choose PDL when you have accounts to match or search programmatically and can own the mapping and exception process. Choose Coresignal when your team will operate structured company data at a scale where API or dataset delivery is part of the product decision.
If none of those statements describes your real bottleneck, postpone the purchase. Write down the exact account decision a rep cannot make today, test that field on 300 representative companies, and ask for the plan and usage rights that cover it. A good source should reduce wrong-account research and improve a sales decision; merely adding more columns to the CRM is not the outcome.
Use the Clay Waterfall Enrichment Guide to combine company matching and selective enrichment while retaining the source of each field.
FAQ
Is company data the same as contact data?
No. Company data describes an organisation, entity, hierarchy or event. Contact data identifies a person and potentially a way to reach them. Some sellers offer both, but a correct company match does not establish a current role, valid email or lawful use of a contact field. Budget and quality-test the stages separately.
Which identifier should be the primary CRM account key?
Keep your own stable CRM ID as the internal key. Store provider identifiers, domains and legal names as mapped external attributes, with effective dates where relationships change. A D-U-N-S number can be useful for D&B linkage; a domain can be useful for enrichment. Neither should silently replace the sales team's rule for what constitutes one selling account.
Is a funding round a buying-intent signal?
It is a dated reason to research an account. It does not prove interest in your category. Check the announcement, stage and likely business change, then find evidence connecting that change to your offer before using it in outreach.
Should the newest provider value overwrite the CRM?
Only where the field has an agreed owner and the match is strong enough. Filling an empty website is different from changing the ultimate parent of a strategic account. Store the previous value, source and timestamp; route ambiguous or consequential changes to review.
How often should company records refresh?
Set cadence by the field and use. A legal identifier may change rarely; employee bands, funding events and websites can change faster. Record when a value was observed and when it was last checked. Confirm whether the vendor delivers changes, a full refresh or only responses to new queries.
Can one provider cover every account-data need?
Sometimes a narrow use case can. For broader research, entity identity, dated events, firmographics and verified contacts are separate jobs. Start with the missing decision, buy the smallest rights and field set that solves it, and add another source only when a representative test shows a real gap.
